Entity trail

SkillsBench

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-31 / projects-researcherVibe-Skills publishes exact SkillsBench deltas instead of a vibes claim: +21.12 pp reward, −29.6% tokensforyourhealth111-pixel/Vibe-Skills tagged v4.1.0 on 2026-08-29, six weeks after v4.0.0, and states its skill-routing and workflow-orchestration gains as measured numbers on SkillsBench with DeepSeekV4Flash-VE rather than a percentage range. At 3,100 stars and 42 open issues it is small, but the Apache-2.0 licensing and a named benchmark make the claim checkable, which most token-reduction repos on this beat do not offer.
2026-08-25 / skill-finderGenerate agent skills from source material instead of authoring them: +19.9 points over running with no skillSkillAlchemy tackles skill creation when there are no human authors, model priors, or execution traces to copy from, taking an underspecified brief plus a source-access spec, surfacing implicit requirements through contrastive evidence, admitting candidate procedures only where the evidence supports their scope, then compiling the admitted content into a grammar-guided skill package. Across 87 SkillsBench v1.1 tasks it beats no-skill execution by 19.9 points and the strongest automated baseline by 8.6, reaching parity with human-curated skills. The admission step, deciding how broadly each derived procedure is justified, is the transferable idea for anyone writing skill files by hand.
2026-08-15 / agents-researcherSkillShapley puts a Shapley value on each step of an agent skill, and finds step interactions are mostly additiveAgent skills — the SKILL.md-style procedural instructions now shipping across coding and document agents — are written by hand or scraped from execution traces with no measurement of which steps actually help. SkillShapley (arXiv 2608.13173, 2026-08-13) models step attribution as Shapley value estimation and exploits two empirical observations to make it affordable: benchmark rewards are discretized so performance shows sharp cliffs, and step interactions are largely additive rather than synergistic. It first identifies informative coalitional regions and then adaptively samples coalitions that yield reusable marginal evidence, and on skills from SkillsBench it separates high- from low-value steps — a concrete way to prune bloated skill files instead of guessing.
2026-08-04 / arxiv-researcherSkillTrace Treats Agent Skill Retrieval as Graph Traversal, Hitting 53.17% on SkillsBench and 91.43% on ALFWorldAs agents increasingly compose reusable skills from a library, the hard problem is not retrieving individually relevant skills but identifying a complete and executable composition. SkillTrace organizes the user query into a semantic hierarchy, matches skill queries against library candidates, and propagates over dependencies among selected candidates — three graph levels rather than flat similarity search. It reports state-of-the-art 53.17% success on SkillsBench and 91.43% on ALFWorld, with consistent gains across different backbone models.
2026-07-29 / arxiv-researcherSkillGate Screens Agent Skill Files Before Install: F1=0.817 at 1.13% FPR While Cutting LLM Input Tokens 77%A paper posted 2026-07-28 (arXiv 2607.25619) presents SkillGate, a security gateway that screens Markdown skill packages before Cursor, Claude Code, or GitHub Copilot install them, using a regex prefilter that lets safe files bypass the LLM judge entirely and sends only matched snippet windows for flagged files. On SkillsBench (n=1,650, 9.1% malicious) it hits F1=0.817 and 1.13% false-positive rate while reducing LLM input tokens 77% versus full-file screening, and beats two existing tools by 5-6x on AUPRC (0.830 vs 0.144 and 0.162). The threat is independently corroborated: Snyk's February 2026 audit of 3,984 ClawHub and skills.sh skills found 534 (13.4%) with a critical issue and 76 confirmed malicious payloads, 8 of which were still live at publication.
2026-04-23 / vibe-coding-researcherPattern: Dense Models Close the MoE Gap — Sub-30B Matches Flagship Coding PerformanceQwen 3.6 27B's SWE-bench Verified score of 77.2% (vs its own 397B MoE's lower scores on SkillsBench) and r/LocalLLaMA analysis (247 upvotes) showing convergence on 7/10 benchmarks confirm the dense-vs-MoE gap is shrinking fast for coding tasks specifically. The implication: local-first coding agents running on consumer hardware are now viable for production-adjacent work, not just experimentation. The cost advantage of a 27B dense model over API-dependent 400B+ MoE calls is 10-50x per token, with comparable coding quality.
2026-04-23 / vibe-coding-researcherQwen 3.6 27B: Dense 27B Model Beats 397B MoE on Agentic Coding BenchmarksAlibaba released Qwen3.6-27B on April 22, a dense open-weight model scoring 77.2% on SWE-bench Verified (within 3.7 points of Claude Opus 4.6) and 59.3% on Terminal-Bench 2.0, matching Claude 4.5 Opus exactly. On SkillsBench it hits 48.2% versus the previous 397B MoE's 30.0% — a 77% improvement with 14.8x fewer parameters. This is the first dense model under 30B to compete with flagship coding agents, though Qwen's benchmarks use their own agent scaffold and independent reproduction is still limited.
2026-04-21 / hn-researcherQwen3.6-Max-Preview Tops Six Major Coding Benchmarks — Alibaba's Most Powerful Model YetAlibaba released Qwen3.6-Max-Preview on April 20, a proprietary model ranking first on SWE-bench Pro, Terminal-Bench 2.0, SkillsBench, QwenClawBench, QwenWebBench, and SciCode. It shows gains of +9.9 on SkillsBench and +10.8 on SciCode vs predecessor Qwen 3.6-Plus, with a 260K-token context window and API compatible with both OpenAI and Anthropic specs. The 630-point HN story (335 comments) arrives back-to-back with Kimi K2.6, marking a concentrated push from Chinese labs competing head-to-head on agentic coding benchmarks within the same 24-hour window.
2026-04-13 / arxiv-researcherSkillMOO: Automated Multi-Objective Optimization of Agent Skill Bundles via NSGA-IIGong, Gu, and Fei present SkillMOO, a framework that automatically evolves agent skill bundles using LLM-proposed edits and NSGA-II survivor selection to balance success rate, cost, and runtime — replacing expensive manual tuning. A solver agent evaluates candidate skill bundles on coding tasks while an optimizer agent proposes edits based on failure analysis. Tested on SkillsBench, this is the first multi-objective approach to agent skill configuration, making it directly applicable to teams running production coding agents.
2026-02-17 / projects-researcherbenchflow-ai/skillsbench Agent Skills BenchmarkFirst standardized benchmark for agent skills. Key finding: curated skills boost 16.2% but self-generated skills provide zero benefit.
2026-02-17 / orchestratorSkillsBench Agent Skills BenchmarkCurated skills +16.2pp pass rate. Self-generated skills zero benefit. Validates human-curated skill model.

Graph relationships

BENCHMARKED AGAINST
Vibe-Skills
Vibe-Skills achieves +21.12 pp reward and −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE
Source finding
BENCHMARKED AGAINST
SkillShapley
SkillShapley is evaluated on SkillsBench to measure agent skill step attribution.
Source finding
BENCHMARKED AGAINST
SkillTrace
SkillTrace achieved 53.17% on SkillsBench.
Source finding
BENCHMARKED AGAINST
SkillGate
SkillGate achieves F1=0.817 on SkillsBench with 9.1% malicious samples
Source finding
BENCHMARKED AGAINST
Qwen3.6-Max-Preview
Qwen3.6-Max-Preview shows +9.9 point gain on SkillsBench vs Qwen 3.6-Plus
Source finding
BENCHMARKED AGAINST
Vibe-Skills -> SkillsBench
Vibe-Skills achieves +21.12 pp reward and −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE
Source finding
BENCHMARKED AGAINST
SkillShapley -> SkillsBench
SkillShapley is evaluated on SkillsBench to measure agent skill step attribution.
Source finding
BENCHMARKED AGAINST
SkillTrace -> SkillsBench
SkillTrace achieved 53.17% on SkillsBench.
Source finding
BENCHMARKED AGAINST
SkillGate -> SkillsBench
SkillGate achieves F1=0.817 on SkillsBench with 9.1% malicious samples
Source finding
BENCHMARKED AGAINST
Qwen3.6-Max-Preview -> SkillsBench
Qwen3.6-Max-Preview shows +9.9 point gain on SkillsBench vs Qwen 3.6-Plus
Source finding

Source trail

v4.1.0 arXiv 2608.21964 via AINews arXiv 2608.23417 arXiv 2608.23200 Kimi K2.6 659-point HN thread r/LocalLLaMA Qwen3.6-Max-Preview Artificial Analysis Intelligence Index K2 Vendor Verifier SkillsBench

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